3D tomographic reconstructions require projection images as input. A projection image assumes that an object of interest is translucent to a source of exposure such as a light source transmitted through the object of interest. The projection image, then, comprises an integration of the absorption by the object along a ray from the source to the plane of projection. Light in the visible spectrum is used as a source of exposure in optical projection tomography.
In the case of producing projections from biological cells, the cells are typically stained with hematoxyln, an absorptive stain that attaches to proteins found cell chromosomes. Cell nuclei are approximately 15 microns in diameter, and in order to promote reconstructions of sub-cellular features it is necessary to maintain sub-micron resolution. For sub-micron resolution, the wavelength of the illuminating source is in the same spatial range as the biological objects of interest. This can result in undesirable refraction effects. As a result a standard projection image cannot be formed. To avoid these undesirable effects, as noted above, the camera aperture is kept open while the plane of focus is swept through the cell. This approach to imaging results in equal sampling of the entire cellular volume, resulting in a pseudo-projection image. A good example of an optical tomography system has been published as United States Patent Application Publication 2004-0076319, on Apr. 22, 2004, corresponding to pending U.S. patent application Ser. No. 10/716,744, filed Nov. 18, 2003, to Fauver, et al. and entitled “Method and Apparatus of Shadowgram Formation for Optical Tomography.” U.S. patent application Ser. No. 10/716,744 is incorporated herein by reference.
Pattern Noise
Pattern noise represents a kind of distortion that is fixed and present to the same degree for all pseudo-projection images acquired in any optical tomography system. The source of this distortion is any component in the optical path from illumination to the image formation that causes light to deviate from its ideal path in a way that is consistent from projection to projection. Pattern noise does not arise from the cell or any components in the cell-CT that are in movement during collection of the pseudo-projection images.
Referring, for example, to FIG. 2, a typical pseudo-projection image exhibiting some causes of pattern noise is shown. These include dust and illumination variation. Also shown in FIG. 2 are two cells C1, C2 embedded in an optical gel. In a system employing a CCD camera for acquiring pseudo projections or the like sources of pattern noise include:                1. Non-constant illumination,        2. Dust on a CCD camera,        3. Non-uniformity in the CCD camera response, and        4. Distortions in illumination arising from dirt/debris on the reflecting surfaces encountered in the optical path.        
Referring now to FIG. 2A, there shown is a selected portion 40 of the pseudo-projection image that has been enhanced as section 40A to better visually illustrate some subtle effects of pattern noise. Section 40A exhibits more subtle distortion that results from dirt and debris on the reflecting surfaces in the optical path. This distortion is exemplified by taking a segment of the pseudo projection and expanding it to fill the entire space gray scale dynamic range. Note the mottling distortion in the background 44.
Distortions Arising from Pattern Noise
Using an optical tomography system as described in Fauver, pseudo-projection images are formed as an object, such as a cell, is rotated. The formed pseudo-projection images are back-projected and intersected to form a 3D image of the cell. The pattern noise in the pseudo projections is also intersected and results in a noise that is additive to the reconstruction of the object of interest. While noise in each pseudo projection may be rather small, in the resulting reconstruction this noise may be quite large as the patterning may reinforce in a constructive way across multiple pseudo projections.
Referring now to FIG. 3, a reconstructed slide that has been enhanced to show the effect of the pattern noise on a reconstructed image is shown. The swirling pattern 30 in the background is one obvious manifestation of pattern noise.
Unfortunately, previously known techniques for spatial filtering do not adequately correct images because they do not effectively address the causes of pattern noise. Spatial filtering does not adequately correct for low frequency illumination variations. Further, spatial filtering does not adequately remove impulse distortions, arising from dust. Further still, the spatial frequency of pattern noise in the form of mottling is in the same range as other features whose 3D reconstruction is desired. Consequently a different approach to pattern noise removal is needed.
The present invention described herein provides, for the first time, a new and novel system and method for removing the detrimental effects of pattern noise in medical imagers.